CN115422496A - Combined correction identification method for carrier rocket mass and thrust parameters under thrust fault - Google Patents

Combined correction identification method for carrier rocket mass and thrust parameters under thrust fault Download PDF

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CN115422496A
CN115422496A CN202210979915.6A CN202210979915A CN115422496A CN 115422496 A CN115422496 A CN 115422496A CN 202210979915 A CN202210979915 A CN 202210979915A CN 115422496 A CN115422496 A CN 115422496A
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谭述君
于海森
毛玉明
刘锦凡
刘浩
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Shanghai Aerospace System Engineering Institute
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Abstract

The invention discloses a joint correction identification method for carrier rocket mass and thrust parameters under a thrust fault, which comprises the following steps: establishing a carrier rocket mass center motion equation and a mass consumption equation for identification; establishing an observation equation according to the apparent acceleration information measured by the carrier rocket inertia sensitive device as the observed quantity of identification; in each calculation period, the rocket mass at the next moment is identified by using the identification result of the thrust parameter at the current moment through Kalman filtering, and the thrust parameter at the next moment is estimated by using the identification result of the rocket mass at the next moment through recursive least squares of fading factors. The identification method based on the combination of the recursive least squares of the fading factors and the Kalman filtering identifies the mass and thrust parameters of the rocket in the flight process in a combined manner, and can effectively identify the mass and thrust parameters of the carrier rocket under the thrust fault.

Description

Combined correction identification method for carrier rocket mass and thrust parameters under thrust fault
Technical Field
The invention belongs to the field of online identification of thrust faults of a typical dynamics system in the flight process of an aircraft, and particularly relates to a joint correction identification method for carrier rocket mass and thrust parameters under the condition of the thrust faults.
Background
The carrier rocket is a main component of a space transport system, is a main tool for human entering space at present, and is a foundation stone for developing space technology and ensuring space safety. The carrier rocket system has complex structure and high launching cost, and is very important for ensuring the safety and the reliability of the carrier rocket system. The power system is one of the key systems of the carrier rocket, in the failed launching cases at home and abroad, a considerable part of the power system is power system faults, and the non-fatal power system faults are generally thrust descent or shutdown. The thrust of the engine is reduced or shut down, so that the thrust of the rocket is unbalanced to generate interference torque, the propellant is slowly consumed, the mass distribution of the rocket body is uneven, and the control capability of a control system is reduced. In fact, such non-fatal powertrain failures are not irreversible, and the rocket has the ability to continue to perform tasks after the rocket thrust drops, so that timely diagnosis of the magnitude of the thrust failure and the current mass of the rocket facilitates subsequent control reconfiguration and flight task adjustment.
Methods of fault diagnosis mainly include model-based, signal-based and knowledge-based methods. The fault diagnosis method based on the model has been widely researched and can be subdivided into a parameter estimation method, a state estimation method, an equivalent space method and the like. For example, the state is estimated by using kalman filtering, and the parameter can also be estimated by using the augmented kalman filtering, but the augmented kalman filtering assumes that the parameter vector to be estimated is an invariant state vector, and thus, the variable parameter cannot be accurately estimated, especially when the parameter to be estimated is mutated. In the field of parameter identification, the least square method is the most widely used estimation method and can be used for dynamic, static, linear and nonlinear systems.
There are two main aspects to the direction of carrier rocket thrust parameter identification: one is to estimate performance parameters by utilizing telemetered engine combustion chamber pressure and a pre-established internal trajectory program, so as to master the real performance of the engine and serve the research and development of the engine and the rocket; and the other method is to use the visual acceleration, rocket speed, position and quality information for identification, thereby providing information for subsequent control reconstruction and flight task adjustment. In the existing research for identifying rocket thrust by using information such as rocket apparent acceleration and the like, the quality of a rocket is assumed to be known, but the real-time quality information in the rocket flight process cannot be measured. Especially, when the thrust of the rocket fails, the consumption speed of the fuel deviates from the preset speed, so that the mass of the rocket has corresponding deviation, and the stability of a control system is influenced.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a joint correction identification method suitable for the mass and thrust parameters of a carrier rocket under the thrust fault, namely an identification method based on the combination of recursive least squares of fading factors and Kalman filtering, which can effectively identify the mass and thrust parameters of the carrier rocket under the thrust fault.
The technical scheme adopted by the invention for solving the technical problem is as follows: a joint correction identification method for carrier rocket mass and thrust parameters under thrust faults comprises the following steps: establishing a carrier rocket mass center motion equation and a mass consumption equation for identification; establishing an observation equation according to the apparent acceleration information measured by the rocket inertia sensitive device as the observed quantity of identification; in each calculation period, the rocket mass at the next moment is identified by using the identification result of the thrust parameter at the current moment through Kalman filtering, and the thrust parameter at the next moment is estimated by using the identification result of the rocket mass at the next moment through recursive least squares of fading factors.
Further, the method specifically comprises the following steps:
establishing a carrier rocket mass center motion equation and a mass consumption equation:
Figure BDA0003800024490000021
wherein: r meterRepresents the vector of the rocket from the earth center, and r = [ r ] x r y r z ] T (ii) a v represents the velocity vector of the rocket, and v = [) x v y v z ] T (ii) a μ represents a gravitational constant; f represents the total thrust of the rocket; u represents the thrust direction vector input, and m represents the rocket mass; c represents a process noise distribution matrix; w represents process noise; i is sp Representing rocket specific impulse; g is a radical of formula 0 Representing gravitational acceleration at sea level;
the observation equation for apparent acceleration is established as follows:
y＝Fu/m+d
wherein y represents observed apparent acceleration information; d represents the measurement noise of the apparent acceleration. Recording an observation matrix h (m, u) = u/m;
selecting the state quantity x = [ r ] x r y r z v x v y v z m] T Then the mass m = x of the rocket 7 And converting the rocket equation and the observation equation into a state space equation:
Figure BDA0003800024490000022
wherein F (x, u, F) and g (x, u, F) represent respective functions;
jacobian matrix equations for F (x, u, F) and g (x, u, F)
Figure BDA0003800024490000023
Setting process noise variance Q w Observing the variance R of the noise d The fading factor ρ;
initial value of initial state estimation
Figure BDA0003800024490000031
Initial value of mass
Figure BDA0003800024490000032
Initial value of thrust parameter estimation
Figure BDA0003800024490000033
Initializing an initial value of a state estimation covariance matrix as P (0); initializing an initial value of a least square covariance matrix to be P Ls (0)；
Prediction of state estimate at time k + 1:
Figure BDA0003800024490000034
where Δ t = t k+1 -t k ；
Calculating a transfer matrix:
Φ(k+1|k)＝e A(k+1)Δt
calculating a discrete noise distribution matrix:
Γ(k+1|k)＝Φ(k+1|k)C(k+1)Δt
and (3) calculating a covariance matrix corresponding to the state estimated value:
Figure BDA0003800024490000035
calculating a Kalman filtering gain matrix:
K(k+1)＝P(k+1|k)H T (k+1)[H(k+1)P(k+1|k)H T (k+1)+R d (k+1)] -1
calculating a state estimation covariance matrix at the moment k + 1:
P(k+1)＝[I-K(k+1)H(k+1)]P(k+1|k)
wherein I represents a unit array of corresponding state dimensions;
calculating a state filtering value at the moment k + 1:
Figure BDA0003800024490000036
obtaining the quality estimation value at the moment k +1 by using the state filtering value at the moment k +1
Figure BDA0003800024490000037
Namely:
Figure BDA0003800024490000038
calculating a least squares gain matrix:
Figure BDA0003800024490000039
in which I M A unit array representing a corresponding observation dimension;
computing a least squares covariance matrix at time k +1
Figure BDA0003800024490000041
Calculating the thrust parameter estimation value at the k +1 moment:
Figure BDA0003800024490000042
if k is less than N, k = k +1, and a state estimated value of predicting k +1 moment is returned; otherwise, the circulation is finished, and the identification result of the carrier rocket mass and thrust parameters is output.
Further, a (k + 1) in the transition matrix is specifically: predicting the state of k +1 time
Figure BDA0003800024490000043
Vector input u (k + 1) at time k +1 and thrust parameter estimation value at time k
Figure BDA0003800024490000044
Substituting into matrix equation A yields A (k + 1).
Further, H (k + 1) in the kalman filter gain matrix is specifically: state prediction value for predicting k +1 moment
Figure BDA0003800024490000045
Vector input u (k + 1) at time k +1 and thrust parameter estimation value at time k
Figure BDA0003800024490000046
Substituting into matrix equation H yields H (k + 1).
Further, in the least square gain matrix
Figure BDA0003800024490000047
The method specifically comprises the following steps: using quality estimates at time k +1
Figure BDA0003800024490000048
Substituting the sum vector input u (k + 1) into the observation matrix to obtain
Figure BDA0003800024490000049
Furthermore, linear interpolation is carried out on the visual acceleration observed quantity so as to increase the total number of sampling points and reduce the identification step length.
The beneficial effects of the invention include: the combined correction identification method combining least square and Kalman filtering is provided, so that the mass and thrust parameters of the carrier rocket under the thrust fault can be effectively identified, and meanwhile, the method is also suitable for the online identification of the mass and thrust parameters of the aircraft under the normal flight condition; the provided method for improving the interpolation of the sampled observation data can effectively improve the traceability of the identification. And the appropriate fading factor and interpolation step length are selected, so that the trackability of the parameter identification method can be improved within the acceptance range of the parameter precision error.
The method can identify the thrust sudden-change faults of the aircrafts such as the rocket and the like, has the characteristics of simple structure, concise design process, high identification precision and the like, has application prospect in identifying the mass and thrust faults of the carrier rocket, and provides reference for identifying the mass and thrust parameters of the rocket.
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FIG. 1 is a diagram of a combined correction and identification scheme for state quantities such as mass and thrust parameters of a launch vehicle according to the present invention;
FIG. 2 is a flowchart of a joint correction identification method for the carrier rocket mass and thrust parameters under thrust failure.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The invention provides a combined correction identification method combining least square and Kalman filtering aiming at identification of the mass and thrust parameters of a carrier rocket, and the method is particularly suitable for online identification of the mass and thrust parameters of an aircraft under a thrust fault and is also suitable for online identification of the mass and thrust parameters of the aircraft under a normal flight condition.
The joint identification method for the mass and thrust parameters of the carrier rocket comprises the following steps:
establishing a rocket mass center motion equation and a rocket mass consumption equation for identification aiming at the problem of joint identification of the carrier rocket mass and thrust;
according to apparent acceleration information measured by the rocket inertia sensitive device, the apparent acceleration information is used as an identified observation equation;
in order to improve the identification trackability, the sampled visual acceleration information can be interpolated, so that the identification step length can be reduced, and the abrupt change tracking capability of the method can be improved;
the rocket mass is identified through Kalman filtering by using the thrust parameter identification result, and the thrust parameter is estimated through recursive least squares by using the rocket mass identification result, so that the joint identification of the mass and the thrust parameter is realized.
Example 1
This example studies the motion of the center of mass of the vacuum section of the rocket, assuming that the engine's specific impulse is known throughout. The joint identification method for the carrier rocket mass and thrust parameters comprises the following steps:
step 1: establishing a rocket mass center motion equation (taking a vacuum section as an example) and a mass consumption equation for identification aiming at the joint identification problem of the carrier rocket mass and thrust:
Figure BDA0003800024490000051
wherein: r represents the vector of the rocket from the geocentric, and r = [ r ] x r y r z ] T (ii) a v represents the velocity vector of the rocket, and v = [ v = x v y v z ] T (ii) a μ represents a gravitational constant; f represents the total thrust of the rocket; u represents a thrust direction vector input, and u = [ u = [ [ u ] 1 u 2 u 3 ] T (ii) a m represents the mass of the rocket; c represents a process noise distribution matrix; w represents process noise; i is sp Representing the specific impulse of the rocket; g 0 Representing the acceleration of gravity at sea level.
Step 2: the apparent acceleration information measured by the inertia sensitive device is used as the observed quantity of identification, and an observation equation is established as follows:
y＝Fu/m+d (2)
wherein y represents observed apparent acceleration information; d represents the measurement noise of the apparent acceleration. Noting the observation matrix h (m, u) = u/m, the observation equation can also be written as:
y＝h(m,u)F+d (3)
and 3, step 3: selecting the state quantity x = [ r ] x r y r z v x v y v z m] T Then there is the rocket mass m = x 7 . The rocket equation and the observation equation are converted into a state space equation in the form of:
Figure BDA0003800024490000061
wherein F (x, u, F) and g (x, u, F) represent corresponding functions, and the specific expression forms are as follows:
Figure BDA0003800024490000062
Figure BDA0003800024490000063
and 4, step 4: jacobian matrix equations for F (x, u, F) and g (x, u, F)
Figure BDA0003800024490000064
The non-zero elements in the matrix equations a, H are respectively expressed as follows:
A(1,4)＝1，A(2,5)＝1，A(3,6)＝1，
Figure BDA0003800024490000065
Figure BDA0003800024490000071
A(4,7)＝-Fu 1 m -2 ，
Figure BDA0003800024490000072
Figure BDA0003800024490000073
Figure BDA0003800024490000074
A(5,7)＝-Fu 2 m -2 ，
Figure BDA0003800024490000075
Figure BDA0003800024490000076
A(6,7)＝-Fu 3 m -2 (7)
H(1,7)＝-Fu 1 m -2
H(2,7)＝-Fu 2 m -2 ，
H(3,7)＝-Fu 3 m -2 (8)
and 5: setting process noise variance Q w Observing the variance R of the noise d The fading factor ρ.
Step 6: initial value of initial state estimation
Figure BDA0003800024490000077
Then there is an initial value of quality
Figure BDA0003800024490000078
Initial value of thrust parameter estimation
Figure BDA0003800024490000079
Setting P to represent a state estimation covariance matrix, and initializing an initial value of the state estimation covariance matrix to be P (0); let P Ls Representing the least square covariance matrix, then initializing the initial value of the least square covariance matrix as P Ls (0) And has:
Figure BDA00038000244900000710
the following steps 7 to 20 are a calculation cycle of the joint correction identification method, which is a process that is based on the above steps and then continuously circulates in the following calculation cycle. The total number of samples is N.
Preferably, the total number of the sampling points can be increased by performing linear interpolation on the original sampling data, namely the observed quantity of the visual acceleration, so that the identification step length is reduced, and the identification tracking capability of the method is improved.
And 7: estimating value according to state of k time
Figure BDA00038000244900000711
And thrust parameter estimation
Figure BDA00038000244900000712
Prediction of state estimate at time k + 1:
Figure BDA00038000244900000713
where Δ t = t k+1 -t k 。
Step 8, predicting the state estimation value at the k +1 moment
Figure BDA0003800024490000081
Vector input u (k + 1) at time k +1 and thrust parameter estimation value at time k
Figure BDA0003800024490000082
Substituting into matrix equation A yields A (k + 1).
And step 9: calculating a transfer matrix:
Φ(k+1|k)＝e A(k+1)Δt (11)
step 10: calculating a discrete noise distribution matrix:
Γ(k+1|k)＝Φ(k+1|k)C(k+1)Δt (12)
step 11: and calculating a covariance matrix corresponding to the state estimated value according to the state estimation covariance matrix P (k) at the time k:
Figure BDA0003800024490000083
step 12: state prediction value for predicting k +1 moment
Figure BDA0003800024490000084
Vector input u (k + 1) at time k +1 and thrust parameter estimation value at time k
Figure BDA0003800024490000085
Substitution into matrix equation H yields H (k + 1).
Step 13: calculating a Kalman filtering gain matrix:
K(k+1)＝P(k+1|k)H T (k+1)[H(k+1)P(k+1|k)H T (k+1)+R d (k+1)] -1 (14)
step 14: calculating a state estimation covariance matrix at the moment k + 1:
P(k+1)＝[I-K(k+1)H(k+1)]P(k+1|k) (15)
where I represents the corresponding state dimension unit matrix.
Step 15: correcting the estimated state according to the observed value at the moment k +1 to obtain a state filtering value at the moment k + 1:
Figure BDA0003800024490000086
step 16: obtaining the quality estimation value at the moment k +1 by using the state filtering value at the moment k +1
Figure BDA0003800024490000087
That is to say that the first and second electrodes,
Figure BDA0003800024490000088
and step 17: using quality estimates at time k +1
Figure BDA0003800024490000089
Substituting the sum vector input u (k + 1) into the observation matrix in the step 2 to obtain
Figure BDA00038000244900000810
And calculating a least squares gain matrix:
Figure BDA00038000244900000811
wherein I M Representing the corresponding observation dimension unit matrix.
Step 18: computing a least squares covariance matrix at time k +1
Figure BDA0003800024490000091
Step 19: calculating the thrust parameter estimation value at the k +1 moment:
Figure BDA0003800024490000092
step 20: if k < N, k = k +1, returning to step 8; otherwise, the circulation is finished, and the rocket mass and thrust parameter identification result is output.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention.

Claims (6)

1. A joint correction identification method for carrier rocket mass and thrust parameters under thrust faults is characterized by comprising the following steps: establishing a rocket centroid motion equation and a rocket centroid mass consumption equation for identification; establishing an observation equation according to the apparent acceleration information measured by the rocket inertia sensitive device as the observed quantity of identification; in each calculation period, the rocket quality at the next moment is identified by using the identification result of the thrust parameter at the current moment through Kalman filtering, and the thrust parameter at the next moment is estimated by using the identification result of the rocket quality at the next moment through fading factor recursive least squares.
2. The joint correction identification method for the mass and thrust parameters of the launch vehicle under thrust failure according to claim 1, characterized by comprising the following steps:
establishing a carrier rocket mass center motion equation and a mass consumption equation:
Figure FDA0003800024480000011
wherein: r represents the vector of the rocket from the center of the earth, and r = [ r ] x r y r z ] T (ii) a v represents the velocity vector of the rocket, and v = [) x v y v z ] T (ii) a μ represents a gravitational constant; f represents the total thrust of the rocket; u represents the thrust direction vector input, and m represents the rocket mass; c represents a process noise distribution matrix; w represents process noise; i is sp Representing the specific impulse of the rocket; g is a radical of formula 0 Representing gravitational acceleration at sea level;
the observation equation is established as follows:
y＝Fu/m+d
wherein y represents observed apparent acceleration information; d represents the measurement noise of apparent acceleration; recording an observation matrix h (m, u) = u/m;
selecting the state quantity x = [ r ] x r y r z v x v y v z m] T Then the mass m = x of the rocket 7 And converting the rocket equation and the observation equation into a state space equation:
Figure FDA0003800024480000012
wherein F (x, u, F) and g (x, u, F) represent respective functions;
jacobian matrix equations for F (x, u, F) and g (x, u, F)
Figure FDA0003800024480000013
Setting process noise variance Q w Observing the variance R of the noise d The fading factor ρ;
initial value of initial state estimation
Figure FDA0003800024480000014
Initial value of mass
Figure FDA0003800024480000015
Initial value of thrust parameter estimation
Figure FDA0003800024480000016
Initializing an initial value of a state estimation covariance matrix as P (0); initializing an initial value of a least square covariance matrix to be P Ls (0)；
Prediction of state estimate at time k + 1:
Figure FDA0003800024480000021
where Δ t = t k+1 -t k ；
Calculating a transfer matrix:
Φ(k+1|k)＝e A(k+1)Δt
calculating a discrete noise distribution matrix:
Γ(k+1|k)＝Φ(k+1|k)C(k+1)Δt
and (3) calculating a covariance matrix corresponding to the state estimated value:
Figure FDA0003800024480000022
calculating a Kalman filtering gain matrix:
K(k+1)＝P(k+1|k)H T (k+1)[H(k+1)P(k+1|k)H T (k+1)+R d (k+1)] -1
calculating a state estimation covariance matrix at the moment k + 1:
P(k+1)＝[I-K(k+1)H(k+1)]P(k+1|k)
wherein I represents a unit array of corresponding state dimensions;
calculating a state filtering value at the moment k + 1:
Figure FDA0003800024480000023
obtaining the quality estimation value at the moment k +1 by using the state filtering value at the moment k +1
Figure FDA0003800024480000024
Namely:
Figure FDA0003800024480000025
calculating a least squares gain matrix:
Figure FDA0003800024480000026
wherein I M A unit array representing a corresponding observation dimension;
computing a least squares covariance matrix at time k +1
Figure FDA0003800024480000027
Calculating the thrust parameter estimation value at the k +1 moment:
Figure FDA0003800024480000031
if k is less than N, k = k +1, and a state estimated value of predicting k +1 moment is returned; otherwise, the circulation is finished, and the identification result of the carrier rocket mass and thrust parameters is output.
3. The joint correction identification method for the mass and thrust parameters of the launch vehicle under thrust failure according to claim 2, characterized in that A (k + 1) in the transfer matrix is specifically: state prediction value for predicting k +1 moment
Figure FDA0003800024480000032
Vector input u (k + 1) at time k +1 and thrust parameter estimation value at time k
Figure FDA0003800024480000033
Substituting into matrix equation A yields A (k + 1).
4. The joint correction identification method for the mass and thrust parameters of the launch vehicle under thrust failure according to claim 2, wherein H (k + 1) in the kalman filter gain matrix is specifically: state prediction value for predicting k +1 moment
Figure FDA0003800024480000034
Vector input u (k + 1) at time k +1 and thrust parameter estimation value at time k
Figure FDA0003800024480000035
Substituting into matrix equation H yields H (k + 1).
5. The method of claim 2, wherein the method comprises a least squares gain matrix, wherein the least squares gain matrix comprises a set of coefficients of mass and thrust parameters of the launch vehicle
Figure FDA0003800024480000036
The method specifically comprises the following steps: using quality estimates at time k +1
Figure FDA0003800024480000037
Substituting the sum vector input u (k + 1) into the observation matrix to obtain
Figure FDA0003800024480000038
6. The joint correction identification method of the mass and thrust parameters of the launch vehicle under thrust fault according to claim 2, characterized in that linear interpolation is performed on the visual acceleration observed quantity to increase the total number of sampling points, reduce the identification step length and improve the tracking performance of the method.
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Publication number Priority date Publication date Assignee Title
CN116879763A (en) * 2023-09-07 2023-10-13 上海融和元储能源有限公司 Battery fault early warning method based on Kalman filtering algorithm

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116879763A (en) * 2023-09-07 2023-10-13 上海融和元储能源有限公司 Battery fault early warning method based on Kalman filtering algorithm

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